Generative AI, Why, How, and Outcomes: A User Adoption Study

• Published in: Association for Information Systems transactions on human-computer interaction 2024-03, Vol.16 (1), p.1-27
• Main Authors: Huy, Le Van, Nguyen, Hien T. T., Vo-Thanh, Tan, Thinh, Nguyen Huu Thai, Dung, Tran Thi Thu
• Format: Article
• Language: English
• Subjects: Chatbots; Generative artificial intelligence; Technology adoption
• ISSN: 1944-3900; 1944-3900
• DOI: 10.17705/1thci.00198

Drawing on the extended unified theory of acceptance and use of technology (UTAUT2) and task-technology fit (TTF) theory, we developed an integrated research model to explore the factors affecting ChatGPT use and its subsequent effects on whether users continue using ChatGPT and recommend it to others. We also examined the main activities of ChatGPT use as well as the moderating role of curiosity in the relationships between various influencing factors and ChatGPT use. We conducted a quantitative study with data that we collected from 671 users in Vietnam. We found that, first, most UTAUT2 and TTF dimensions affected ChatGPT use. Interestingly, contrary to our expectations, effort expectancy, social influence, and trust had no effect on ChatGPT use. Second, ChatGPT use directly influenced intention to continue using ChatGPT and word of mouth (WOM). Third, intention to continue using ChatGPT had a significant effect on WOM. Finally, we found that curiosity acted as a moderator in only three paths from hedonic motivation, facilitating conditions, and performance expectancy to ChatGPT use. With this study, we contribute a unique research model that combines the UTAUT2 and the TTF (including both trust and curiosity) as well as knowledge about users’ behavioral process in technology adoption by examining a comprehensive process, namely, actual usage– continuance intention to use–recommending. Practical implications for ChatGPT providers, policymakers, and business marketers are also discussed.